The Dalai Lama is Right

Technology Does Not Eliminate Human Choice

Technological determinism is a recurrent idea, suggesting that technology is a primary force shaping society. The notion is that once a technology appears, its capabilities and economic logic tend to push society toward particular outcomes. But a survey of Goldman Sachs interns suggests the limits of such thinking. 

About 60 percent of respondents prefer no AI involvement in creative work such as art, music, and books. They are comfortable with some AI assistance for content curation, healthcare, shopping, customer service or personal finance, so long as there is human validation. 

source: Goldman Sachs 

In other words, the survey suggests a human response to AI that is not a simple or passive role, despite much “doomerism” about it. 

To be sure, technology creates possibilities. But that does not necessarily mean it is imperative. The telephone could have eliminated face-to-face meetings, but it didn't. Television could have eliminated newspapers, but it didn't. ATMs could have eliminated bank tellers. The automobile made commuting possible but did not make commuting inevitable.

Calculators changed which mathematical skills were economically valuable, but not lower-order operations. When computation became cheap, computation itself became less economically scarce. The value shifted toward applications, data, organizational knowledge and decisions.

The point is that predictions about deterministic technological outcomes have often been wrong. 

Technology	Deterministic prediction	What actually happened	What it illustrates
Telegraph	Near-instant communication would effectively eliminate the importance of distance and perhaps produce a unified world	Distance became less important for information, but geography, politics, culture and institutions remained extremely important	Technology can reduce one constraint without eliminating others
Telephone	Telephones would largely replace face-to-face interaction	Telephone communication became complementary to physical interaction	People value how interaction occurs, not merely information transfer
Radio	Mass broadcasting would create a common national culture and potentially make older media obsolete	Radio became one medium among many; audiences fragmented and new media emerged	New media usually complement as well as substitute
Television	Television would replace newspapers, radio, books and perhaps traditional education	All survived, although their roles changed substantially	Technologies rarely eliminate an entire human activity simply because they can perform part of it
Automobile	Cheap personal transportation would make cities and mass transit obsolete	Cars produced suburbs and highways, but dense cities, rail and walking remained valuable	Technology interacts with urban design, policy and preferences
Air conditioning	Climate would cease to constrain where people live and work	It transformed settlement patterns, but water, energy, geography, culture and economics continued to matter	Removing one constraint doesn't remove the system of constraints
Nuclear power	Extremely cheap electricity would make energy essentially unlimited and transform society	Nuclear power became important but expensive, politically contested and institutionally constrained	Technical potential ≠ economically or politically realized potential
Computers	Automation would eliminate large portions of clerical and administrative employment	Computers eliminated many tasks but also created enormous new categories of work and increased demand for information-intensive occupations	Productivity and employment aren't mechanically inversely related
ATMs	Banks would need dramatically fewer employees because machines could replace tellers	ATM deployment coincided with changes in branch economics, but banks retained many tellers and expanded some branch networks	Lower cost per transaction can increase total demand
The Internet	Physical retail, newspapers, television and other intermediaries would disappear	They were disrupted, but many adapted; new intermediaries emerged	Disintermediation often produces reintermediation
E-books	Paper books would rapidly disappear	E-books became substantial but physical books remained remarkably persistent	Consumers don't optimize solely for technical efficiency
MP3/digital music	Recorded music would become essentially free and traditional music companies would disappear	Streaming created new subscription and platform models; recorded music remained a major business	New technology changes the business model rather than necessarily destroying the activity
Social media	Universal connectivity would produce greater understanding and democratic consensus	It also produced polarization, fragmentation, communities and entirely new forms of political communication	Technology amplifies social forces rather than determining their direction
Online education	Internet video would make physical universities largely unnecessary	Online education grew enormously, but residential universities remained important for social, credentialing and institutional reasons	Education is an institution, not merely information delivery
Remote work	Once high-quality videoconferencing existed, offices would largely become unnecessary	Remote/hybrid work expanded substantially, but many organizations retained physical workplaces	Work has coordination, social and cultural dimensions
3D printing	Manufacturing would move from centralized factories to individuals producing objects at home	3D printing found important niches, but mass manufacturing remained highly centralized	Unit economics and supply chains matter alongside technical capability

Technology does constrain the choice set, though. Once something becomes dramatically cheaper, faster or more capable, people change their behavior. 

Social media reshapes content production, consumption and value. Automobiles changed the geography of cities.

Artificial intelligence can automate many routine operations. But efficiency is not the only key value for humans. People don't optimize every aspect of life for minimum cost, greatest efficiency or least time. 

Often, value is generated by activity itself. Live music, handmade objects, sports, religious services, face-to-face education, crafts, gardening, cooking and all manner of “live” experiences have intrinsic value, so we keep doing those things. 

So the issue is more “what do you do with more free time?” 

AI might allow us to focus more on purpose, meaning, responsibility, relationships and judgment as the need to spend so much time on instrumental activities decreases. As unfashionable as “religion” might be in some quarters, it is possible its value could become more important. 

Humans make choices, even when technology creates possibilities or conditions. We don't always choose efficiency over other values.

Character.ai: First Domino Falls

Perhaps the character.ai Chapter 11 bankruptcy, which allows it to continue operating while it conducts a sale of its assets, is the first step in a rationalization of the generative artificial intelligence market which virtually all observers would likely agree is inevitable. 

It appears the cost of legal battles are the immediate cause of the move. “Over the past year, Character.ai has taken meaningful steps to sustain and improve its business in the face of significant legal, regulatory, and operational headwinds,” said Colin Adams, company CEO.

The firm appears to have about $50 million to $100 million in liabilities and some $20 million to $25 million in assets such as cash. 

Character.ai has settled multiple lawsuits from families of children who died or attempted suicide after interacting with the firm’s chatbots. In October 2025, the company banned users under age 18 from having “open-ended chats” with its chatbot.

In January, Character.ai settled several lawsuits brought by families of teenagers who allegedly suffered harm. 

In May this year, the Pennsylvania government also sued the company alleging that its chatbot falsely presented itself as a licensed medical professional in the state and gave a “fake Pennsylvania license number” to a user.

Disney also sued for patent infringement, alleging the chatbot used its copyrighted characters without permission. The chatbot allegedly impersonated the likenesses of Frozen’s Elsa, Moana, Spider-Man’s Peter Parker and Star Wars character Darth Vader. 

Disney already has made a $20 million offer for some of Character.ai’s intellectual property, but other bidders could emerge, some observers suggest. 

Eventually, cash burn issues for many would-be contestants will cause many of the firms to follow character.ai. 

Even giant OpenAI has an annual cash burn rate between $14 billion and $17 billion, exceeding its revenues. 

And OpenAI does not expect to reach positive cash flow until about 2030. 

source: Reuters

But that same general argument could be made for all the leading and would-be contenders in the language model market. Generally speaking, investors might well believe that contestants with other existing businesses throwing off cash are better placed to win the battle. 

Conversely, upstarts gambling everything on the model business are arguably more exposed. Consider OpenAI, Anthropic and Perplexity, for example. Anthropic hopes to reach cash flow breakeven in 2028, while Perplexity hopes to do so between 2027 and 2028. 

That will matter greatly if capital availability becomes an issue. Recall the collapse of the dot-com bubble, when startups found they could no longer raise capital to fund their operating losses. 

Metric	OpenAI	Anthropic	Perplexity
Annualized Revenue (ARR)	$50 Billion	$65 Billion	$750 Million
Estimated Annual Burn	$14B to $17B	$5B to $6B	$100M to $150M
Burn % of Revenue	57%	33%	15% to 20%
Breakeven Target	2030	2028	2027 to 2028

Braking The AI Arms Race?

I was talking at lunch recently with some friends about why some leaders of artificial intelligence frontier models might actually favor slowing the rate of such model development, whether it can actually be done, and what constraints might exist.

Google’s development of Gemini 4 Argon illustrates one of the issues. Basically, frontier model development is open ended: it never stops. So that also means development work and investment never stops. It is an arms race.



source: Google

And the problem with arms races is that there is no obvious method of restraint. Competition does not necessarily converge on an efficient stopping point. There may be physical, financial, or technological limits, but there is often no natural point at which the participants independently decide that additional investment is no longer worthwhile.

Each participant's decision changes the payoff to everyone else. The security dilemma is that actions taken defensively by one participant can make the other participant feel less secure. So technological progress can continually move any stopping points.

“Parity” can exist at any level, from small to very large. And there is an asymmetry between the output gains and the input investment. Each additional increment of capability might not be worth all that much (it allows a firm to keep pace) but falling behind could be extremely costly (a firm loses the ability to lead the “winner takes most” market in share or profits).

It’s a positive feedback loop, very much similar to the cat-and-mouse game played by hackers and security professionals.

In other words, firms need to keep investing and keep running simply to stay in the same place, and, over time, customers might have trouble distinguishing between competitor offers.

But economic or financial constraints should eventually develop. Already, frontier model developers are facing resistance from financial interests that want to see revenue produced by all the investment. Political resistance also is growing.

So constraints seemingly will be imposed by external forces, and not market competition. At least so far, the marginal benefit of additional investment arguably outweighs the cost, as frontier models are striving for artificial general intelligence.

So contestants themselves are unlikely to “naturally” restrain investment levels. But external forces seemingly are gaining strength in the form of calls for lawmakers to “do something” about regulating AI. And even industry leader calls for some restraint while justified on ethical concerns, also strengthen the demand for some external regulation.

SpaceXSI, AT&T, Verizon, T-Mobile and Beauty Contests

AT&T, Verizon and T-Mobile have been under pressure since SpaceXSI suggested it would be competing in the mobile phone business, and has followed up by acquiring 800-MHz low-band spectrum ideally suited to inside-building connectivity, an important consideration since satellite-to-phone only works when there is line-of-sight. 

According to Elon Musk, the purchase is "the last critical piece of the spectrum puzzle needed for SpaceX to provide complete phone coverage in America".

On October 9, 2026, T-Mobile US shares plunged 13 percent. the company's worst single-day drop since 2013.

AT&T shares fell approximately almost 10 percent, the worst single-day drop since 2000. Meanwhile, Verizon Communications declined about nine percent,  Verizon's worst single-day loss since July 2002.

Skeptics will argue the impact is out of proportion to the nature of the threat, as SpaceXSI still has to acquire or build a terrestrial transmission infrastructure. 

The impact on SpaceXSI was the opposite. Based on SpaceXSI's current market capitalization of $2.14 trillion, 3.5 percent of its market value is approximately $74.94 billion. So it might be argued that the 3.7-percent increase in market valuation on Oct. 9, 2026, which was about $75 billion, reflects the $8 billion investment in spectrum. 

In other words, SpaceXSI valuation increased about 10 times more than the cost of spectrum acquisition. 

On Oct. 9, SpaceXSI gained $27 billion, even using the low figure of 1.24 percent increase for the day.

The mobile service providers lost about $50 billion. 

You might argue that is an example of the “Keynesian beauty contest” at work. The Keynesian beauty contest is an economic and behavioral finance concept introduced by John Maynard Keynes in 1936 to explain how speculative markets and asset prices fluctuate.

The basic idea is that investors buy some stocks not because of strong company fundamentals, but because they believe others will buy them and drive the price higher.

The analogy is a beauty contest in which judges are rewarded for selecting the most popular choices among all judges, rather than those they may personally find the most attractive. 

As it pertains to SpaceXSI, at least some investors arguably price the company not on its current cash flows or fundamentals, but on what they believe other market participants will pay. 

It is, in other words, a narrative-driven market. We saw this during the dot-com bubble. But it has happened before then, as well. We call these periods “manias” or “bubbles.” 

I’m not arguing we are in a similar bubble at the moment. Maybe we are, maybe we will be, but it is not certain we are at the moment. 

Instead, we do see a beauty contest logic at work, where valuation is less on fundamentals and more on expectations. It might be unfair, but SpaceXSI currently benefits from the beauty contest, while the big three mobile service providers suffer.

Era and Mania	The "Prettiest Face" (The Hype Asset)	The Real Underlying Catalyst	The Beauty Contest Driver	The Collapse Trigger
1636–1637 Tulip Mania	Rare, variegated tulip bulbs (e.g., Semper Augustus).	The introduction of exotic flowers to the Dutch merchant class and early futures contract innovations.	Buyers knew a single bulb couldn't generate income. They bought purely because the broader Dutch public was highly coordinated in bidding up bulb futures, ensuring a "greater fool" would buy it next.	In February 1637, buyers at a routine auction in Haarlem simply failed to show up, breaking the psychological chain of assumed demand.
1720 South Sea Bubble	Shares of the South Sea Company.	A British government debt-for-equity swap scheme wrapped in a theoretical monopoly over South American trade routes.	Investors recognized that actual trade was non-existent. However, because the King and high-ranking politicians were buying and promoting it, citizens bought shares assuming the royal endorsement would force the public to keep bidding it up.	The company's own insiders realized the stock was dramatically overvalued and quietly began liquidating their positions, sparking a mass panic.
1840s Railway Mania	Shares of newly proposed railway companies.	The industrial revolution and the British government relaxing rules on corporate formation, combined with cheap credit.	While railways were revolutionary, the market funded thousands of overlapping, impossible routes. Investors didn't look at track viability; they chased the "railway" tag because newspaper columns constantly validated it.	The Bank of England raised interest rates to combat inflation, draining the cheap credit that speculators relied on to buy shares on leverage.
1920s The Roaring Twenties	Industrial conglomerates, radio technology stocks, and investment trusts.	Widespread adoption of electricity, automobiles, mass media, and the creation of retail margin accounts.	Speculators freely admitted stocks were expensive. They bought because loose margin requirements (borrowing up to 90% of the stock price) meant a massive wave of new retail money was constantly entering the market.	Corporate earnings began cooling in mid-1929, leading to minor margin calls that snowballed into the catastrophic Black Thursday crash of October 1929.
1997–2001 Dot-com Bubble	Any company with a ".com" suffix or internet business plan.	The commercial birth of the World Wide Web and massive telecom network buildouts.	Traditional valuation metrics (P/E ratios) were abandoned. Investors bought companies with zero revenue because they knew institutional mutual funds were suffering from intense FOMO and felt forced to chase internet listings.	Major tech backbones (like Cisco and Intel) reported spending slowdowns, revealing that startup capital was burning out, which ended the assumption of infinite demand.
2024–Present AI Infrastructure Boom	Hardware providers (GPUs), liquid cooling firms, and next-generation data center networks.	The emergence of highly scalable Large Language Models (LLMs) requiring massive parallel computing power.	High-frequency trading algorithms and retail momentum traders target companies based on the size of their "AI capital expenditures." Traders buy not for the immediate dividend yield, but because tech giants are locked in an arms race and must keep buying hardware.	Highly variable; heavily tied to micro-catalysts, structural demand shifts (such as ultra-efficient compute open-source models), or data center budget fatigue.